No abstract

Underwater source localization via passive sonar is a challenging task due to the dynamic and complex nature of the acoustic environment. Different from approaches based on matched-field processing, this work explores broadband underwater source localization within a multitask learning (MTL) framework. Here, each task refers to a robust signal approximation problem over a single frequency. MTL provides a natural framework for exchanging information across the narrowband signal-approximation problems and constructing an aggregate (across frequencies) source-localization map. Efficient algorithms based on block coordinate descent (BCD) are developed for solving the source-localization problem. Complex-valued predictor screening rules for reducing the computational complexity of the algorithm are also developed. These rules discard map locations from the set of possible source locations prior to using BCD. They reduce the computational complexity of the localization algorithm without compromising the localization results. Tests of these approaches on synthetic and real data for the SWellEX-3 environment compare the performance of the proposed algorithm to that of alternative methods.Index Terms-Block coordinate descent, group sparsity, multitask learning, underwater source localization.

In this work a framework is presented for addressing the issue of intermittent communications faced by autonomous unmanned maritime vehicles operating at sea. In particular, this work considers the subject of predictive atmospheric signal transmission over multi-path fading channels in maritime environments. A Finite State Markov Channel is used to represent a Nakagami-m modeled physical fading radio channel. The range of the received signal-to-noise ratio is partitioned into a finite number of intervals which represent application-specific communications states. The Advanced Propagation Model (APM), developed at the Space and Naval Warfare Systems Center San Diego, provides a characterization of the transmission channel in terms of evaporation duct induced signal propagation loss. APM uses a hybrid ray-optic and parabolic equations model which allows for the computation of electromagnetic (EM) wave propagation over various sea and/or terrain paths. These models which have been integrated in the proposed framework provide a strategic and mission planning aid for the operation of maritime unmanned vehicles at sea.

Tracking underwater acoustic sources using passive sonar is a challenging task due to the complex interactions that acoustic signals undergo as they propagate through the undersea environment. Notwithstanding these challenges, methods for tracking acoustic sources, which assume that acoustic environmental information is available, have been proposed. These methods are challenged by their high computational complexity and often do not exploit the temporal structure inherent to the tracking problem. This work proposes a sparsity-driven approach for tracking broadband acoustic sources. Source location maps (SLMs), one per frequency, are sequentially estimated while capturing the temporal dependance between successive SLMs. Coherence across the SLMs' support is enforced to guarantee that the source-location estimates are independent of frequency. An iterative solver based on the proximal gradient method is developed to construct the SLMs. Numerical examples on real data illustrate the performance of the proposed algorithm.